ALBERT

Notes: Purpose - The purpose of this paper is to maintain safe flight and to improve existing deicing (in-flight removal of ice) and anti-icing (prevention of ice accretion) systems under in-flight icing conditions.Design/methodology/approach - A recent academic research on aircraft icing phenomenon is presented. Several wind tunnel tests of an experimental aircraft provided by NASA are used in the neural network training. Five ice-affected parameters are chosen in the light of these experiments and researches. An offline artificial neural network is used as an identification technique. The Kalman filter is used to increase the state measurement's accuracy such that neural network training performance gets better. A linear A340 dynamic model is selected in cruise conditions. This linear model is simulated in time varying manner in terms of changing icing parameters in a system dynamic matrix. The obtained data are used in neural network training and testing.Findings - Airframe icing can grow in many ways and many points on aircraft. In this research, wing leading edge ice occurrence is only considered at the same level in both left and right wings. During ice growth other faults or anomalies are ignored.Originality/value - Existing icing sensors can only provide an indication about possible ice presence. They cannot give information of the exact level of ice. However, the efficiency of current control system of changed model decreases. The proposed technique offers a method to find out the model changes under icing conditions.

Notes: Purpose - The purpose of the paper is to present an approach to detect and isolate the aircraft sensor and control surface/actuator failures affecting the mean of the Kalman filter innovation sequence. Design/methodology/approach - The extended Kalman filter (EKF) is developed for nonlinear flight dynamic estimation of an F-16 fighter and the effects of the sensor and control surface/actuator failures in the innovation sequence of the designed EKF are investigated. A robust Kalman filter (RKF) is very useful to isolate the control surface/actuator failures and sensor failures. The technique for control surface detection and identification is applied to an unstable multi-input multi-output model of a nonlinear AFTI/F-16 fighter. The fighter is stabilized by means of a linear quadratic optimal controller. The control gain brings all the eigenvalues that are outside the unit circle, inside the unit circle. It also keeps the mechanical limits on the deflections of control surfaces. The fighter has nine state variables and six control inputs. Findings - In the simulations, the longitudinal and lateral dynamics of an F-16 aircraft dynamic model are considered, and the sensor and control surface/actuator failures are detected and isolated. Research limitations/implications - A real-time detection of sensor and control surface/actuator failures affecting the mean of the innovation process applied to the linearized F-16 fighter flight dynamic is examined and an effective approach to isolate the sensor and control surface/actuator failures is proposed. The nonlinear F-16 model is linearized. Failures affecting the covariance of the innovation sequence is not considered in the paper. Originality/value - An approach has been proposed to detect and isolate the aircraft sensor and control surface/actuator failures occurred in the aircraft control system. An extended Kalman filter has been developed for the nonlinear flight dynamic estimation of an F-16 fighter. Failures in the sensors and control surfaces/actuators affect the characteristics of the innovation sequence of the EKF. The failures that affect the mean of the innovation sequence have been considered. When the EKF is used, the decision statistics changes regardless the fault is in the sensors or in the control surfaces/actuators, while a RKF is used, it is easy to distinguish the sensor and control surface/actuator faults.

Notes: Poly-β-hydroxybutyrate was determined in lactic acid bacteria belonging to the genera Lactobacillus, Lactococcus, Pediococcus and Streptococcus. Lactobacilli were grown in MRS broth, the others were grown in Elliker broth medium. Cell biomass was obtained by centrifugation. The cell walls were lysed with sodium hypochlorite. Poly-β-hydroxybutyrate was extracted using chloroform in a Soxhlet system. Then it was converted to crotonic acid using sulfuric acid and the amount of crotonic acid was measured spectrophotometrically. The yield of poly-β-hydroxybutyrate (% of cell dry weight) of Lactobacillus species was 6.6–35.8%. The values for Lactococcus, Pediococcus and Streptococcus species were 9.0–20.9, 1.1–8.0 and 6.8–17.2, respectively. It was observed that one of the Lactobacillus species did not produce poly-β-hydroxybutyrate. Generally, Lactobacillus species produced more poly-β-hydroxybutyrate than the other tested bacteria and no significant correlation was observed between poly-β-hydroxybutyrate production and cell density of the cultures.